Image processing has been applied to a wide variety of image enhancement issues. There are two crucial problems in fingerprint image analysis. These are feature enhancement and background-foreground segmentation. Current feature enhancement techniques typically target feature points including minute-ridge bifurcations and ridge terminations. These features are the objects most often scrutinized in matching algorithms that extract the feature points on the enrolled fingerprint and attempt to localize the feature point in the input fingerprint.
FIG. 1 illustrates the block diagram of a prior art fingerprint feature point based algorithm. Current techniques first generate a template containing information pertinent to feature points of the fingerprints. These templates are then compared. Image input 101 receives the fingerprint input image. Noise removal stage 102 removes noise from the image input. Feature extraction stage 103 attempts to recognize and extract relevant features form the image. Template generator 104 generates templates for the matching process. Template generator 104 requires a fingerprint input image free from noise so that feature points can be extracted with a high level of confidence.
Fingerprints captured under noisy operational environments including inconsistent contact of finger with sensor, exertion of more than optimal or less than optimal required pressure on the sensor, shear force on the sensor, and sensor defects often tend to lessen the distinction between ridges and valleys. As a result feature extraction stage 103 tends to extract many spurious minutiae. Such spurious minutia degrades the performance of the identification system. Conventional approaches to solve this degradation problem include filtering to reduce ambient noise.
FIG. 2 illustrates the 2D convolution mask conventionally used for low pass filtering the input image. Each pixel of the image is replaced with the weighted average of the neighboring pixels. This removes small-unwanted discontinuities present in the image. This can also blur the image.
Low-pass median filters show excellent performance for images having salt and pepper noise. Unfortunately these filters blur the image reducing the ridge-valley distinction. This effect can be observed in FIG. 3. FIG. 3A is an example input image. FIG. 3B is the corresponding filtered output image.
Detection of ridges and valleys, an important step in the feature extraction process, involves segmentation of the image background (valleys) and foreground (ridges). In conventional techniques a thresholding operation converts a gray-scale image into a black and white image. The threshold can be determined adaptively, but this process is clearly not perfect at all times. Unwanted continuities or discontinuities in the ridge-valley structures often result. This occurs from information loss during color conversion and because thresholding is independent of pixel-neighborhood relationships. Noise added due to this down conversion has to be reduced to achieve accurate feature extraction.